app.py

import streamlit as st
import pickle
import pandas as pd

st.title("Classification the Iris")

sepal_length = st.text_input('Sepal_Length (cm)')
sepal_width = st.text_input('Sepal_Width (cm)')
petal_length = st.text_input('Petal_Length (cm)')
petal_width = st.text_input('Petal_Width (cm)')

dataframe = pd.DataFrame({"sepal length (cm)":[sepal_length],"sepal width (cm)":[sepal_width],'petal length (cm)':[petal_length],'petal width (cm)':[petal_width]})

if st.button('Prediction'):
    with open('model.pkl', 'rb') as file:
        loaded_model = pickle.load(file)
        final_output = loaded_model.predict(dataframe)
        if final_output == '0':
            st.write("The output class is setosa")
        elif final_output == '1':
            st.write("The output class is versicolor")
        elif final_output == '2':
            st.write("The output class is virginica")